{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline for the anomaly detection on the SKAB using Hotelling's statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# libraries importing\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# additional modules\n",
    "import sys\n",
    "sys.path.append('../utils')\n",
    "from t2 import T2\n",
    "from evaluating import evaluating_change_point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# benchmark files checking\n",
    "all_files=[]\n",
    "import os\n",
    "for root, dirs, files in os.walk(\"../data/\"):\n",
    "    for file in files:\n",
    "        if file.endswith(\".csv\"):\n",
    "             all_files.append(os.path.join(root, file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# datasets with anomalies loading\n",
    "list_of_df = [pd.read_csv(file, \n",
    "                          sep=';', \n",
    "                          index_col='datetime', \n",
    "                          parse_dates=True) for file in all_files if 'anomaly-free' not in file]\n",
    "# anomaly-free df loading\n",
    "anomaly_free_df = pd.read_csv([file for file in all_files if 'anomaly-free' in file][0], \n",
    "                            sep=';', \n",
    "                            index_col='datetime', \n",
    "                            parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data description and visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A number of datasets in the SkAB v1.0: 34\n",
      "\n",
      "Shape of the random dataset: (1146, 10)\n",
      "\n",
      "A number of changepoints in the SkAB v1.0: 130\n",
      "\n",
      "A number of outliers in the SkAB v1.0: 13241\n",
      "\n",
      "Head of the random dataset:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accelerometer1RMS</th>\n",
       "      <th>Accelerometer2RMS</th>\n",
       "      <th>Current</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Thermocouple</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Volume Flow RateRMS</th>\n",
       "      <th>anomaly</th>\n",
       "      <th>changepoint</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:36</th>\n",
       "      <td>0.027429</td>\n",
       "      <td>0.040353</td>\n",
       "      <td>0.770310</td>\n",
       "      <td>0.382638</td>\n",
       "      <td>71.2129</td>\n",
       "      <td>25.0827</td>\n",
       "      <td>219.789</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:37</th>\n",
       "      <td>0.027269</td>\n",
       "      <td>0.040226</td>\n",
       "      <td>1.096960</td>\n",
       "      <td>0.710565</td>\n",
       "      <td>71.4284</td>\n",
       "      <td>25.0863</td>\n",
       "      <td>233.117</td>\n",
       "      <td>32.0104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:38</th>\n",
       "      <td>0.027040</td>\n",
       "      <td>0.039773</td>\n",
       "      <td>1.140150</td>\n",
       "      <td>0.054711</td>\n",
       "      <td>71.3468</td>\n",
       "      <td>25.0874</td>\n",
       "      <td>234.745</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:39</th>\n",
       "      <td>0.027563</td>\n",
       "      <td>0.040313</td>\n",
       "      <td>1.108680</td>\n",
       "      <td>-0.273216</td>\n",
       "      <td>71.3258</td>\n",
       "      <td>25.0897</td>\n",
       "      <td>205.254</td>\n",
       "      <td>32.0104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09 12:14:41</th>\n",
       "      <td>0.026570</td>\n",
       "      <td>0.039566</td>\n",
       "      <td>0.704404</td>\n",
       "      <td>0.382638</td>\n",
       "      <td>71.2725</td>\n",
       "      <td>25.0831</td>\n",
       "      <td>212.095</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accelerometer1RMS  Accelerometer2RMS   Current  Pressure  \\\n",
       "datetime                                                                        \n",
       "2020-03-09 12:14:36           0.027429           0.040353  0.770310  0.382638   \n",
       "2020-03-09 12:14:37           0.027269           0.040226  1.096960  0.710565   \n",
       "2020-03-09 12:14:38           0.027040           0.039773  1.140150  0.054711   \n",
       "2020-03-09 12:14:39           0.027563           0.040313  1.108680 -0.273216   \n",
       "2020-03-09 12:14:41           0.026570           0.039566  0.704404  0.382638   \n",
       "\n",
       "                     Temperature  Thermocouple  Voltage  Volume Flow RateRMS  \\\n",
       "datetime                                                                       \n",
       "2020-03-09 12:14:36      71.2129       25.0827  219.789              32.0000   \n",
       "2020-03-09 12:14:37      71.4284       25.0863  233.117              32.0104   \n",
       "2020-03-09 12:14:38      71.3468       25.0874  234.745              32.0000   \n",
       "2020-03-09 12:14:39      71.3258       25.0897  205.254              32.0104   \n",
       "2020-03-09 12:14:41      71.2725       25.0831  212.095              33.0000   \n",
       "\n",
       "                     anomaly  changepoint  \n",
       "datetime                                   \n",
       "2020-03-09 12:14:36      0.0          0.0  \n",
       "2020-03-09 12:14:37      0.0          0.0  \n",
       "2020-03-09 12:14:38      0.0          0.0  \n",
       "2020-03-09 12:14:39      0.0          0.0  \n",
       "2020-03-09 12:14:41      0.0          0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dataset characteristics printing\n",
    "print(f'A number of datasets in the SkAB v1.0: {len(list_of_df)}\\n')\n",
    "print(f'Shape of the random dataset: {list_of_df[10].shape}\\n')\n",
    "n_cp = sum([len(df[df.changepoint==1.]) for df in list_of_df])\n",
    "n_outlier = sum([len(df[df.anomaly==1.]) for df in list_of_df])\n",
    "print(f'A number of changepoints in the SkAB v1.0: {n_cp}\\n')\n",
    "print(f'A number of outliers in the SkAB v1.0: {n_outlier}\\n')\n",
    "print(f'Head of the random dataset:')\n",
    "display(list_of_df[0].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# random dataset visualizing\n",
    "list_of_df[1].plot(figsize=(12,6))\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Value')\n",
    "plt.title('Signals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the labels both for outlier and changepoint detection problems\n",
    "list_of_df[1].anomaly.plot(figsize=(12,3))\n",
    "list_of_df[1].changepoint.plot()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method applying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# classifier initializing\n",
    "t2 = T2(scaling=True, using_PCA=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inference\n",
    "predicted_outlier, predicted_cp = [], []\n",
    "for df in list_of_df:\n",
    "    X_train = df[:400].drop(['anomaly','changepoint'], axis=1)\n",
    "    \n",
    "    # classifier fitting\n",
    "    t2.fit(X_train)\n",
    "    \n",
    "    # results predicting\n",
    "    t2.predict(df.drop(['anomaly','changepoint'], axis=1), window_size=5, plot_fig=False)\n",
    "    prediction = pd.Series((t2.T2>2*t2.T2_UCL).astype(int), \n",
    "                                index=df.index).fillna(0)\n",
    "    # predicted outliers saving\n",
    "    predicted_outlier.append(prediction)\n",
    "    \n",
    "    # predicted CPs saving\n",
    "    prediction_cp = abs(prediction.diff())\n",
    "    prediction_cp[0] = prediction[0]\n",
    "    predicted_cp.append(prediction_cp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# true outlier indices selection\n",
    "true_outlier = [df.anomaly for df in list_of_df]\n",
    "\n",
    "predicted_outlier[1].plot(figsize=(12,3), label='predictions', marker='o', markersize=5)\n",
    "true_outlier[1].plot(marker='o', markersize=2)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# true changepoint indices selection\n",
    "true_cp = [df.changepoint for df in list_of_df]\n",
    "\n",
    "predicted_cp[0].plot(figsize=(12,3), label='predictions', marker='o', markersize=5)\n",
    "true_cp[0].plot(marker='o', markersize=2)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False Alarm Rate 12.14 %\n",
      "Missing Alarm Rate 52.56 %\n",
      "F1 metric 0.56\n"
     ]
    }
   ],
   "source": [
    "# binary classification metrics calculation\n",
    "binary = evaluating_change_point(true_outlier, predicted_outlier, metric='binary', numenta_time='30 sec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average delay 0 days 00:00:07.150000\n",
      "A number of missed CPs = 90\n"
     ]
    }
   ],
   "source": [
    "# average detection delay metric calculation\n",
    "add = evaluating_change_point(true_cp, predicted_cp, metric='average_delay', numenta_time='30 sec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intersection of the windows of too wide widths for dataset 16\n",
      "Intersection of the windows of too wide widths for dataset 16\n",
      "Intersection of the windows of too wide widths for dataset 16\n",
      "Intersection of the windows of too wide widths for dataset 18\n",
      "Intersection of the windows of too wide widths for dataset 18\n",
      "Intersection of the windows of too wide widths for dataset 18\n",
      "Intersection of the windows of too wide widths for dataset 19\n",
      "Intersection of the windows of too wide widths for dataset 19\n",
      "Intersection of the windows of too wide widths for dataset 19\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 23\n",
      "Intersection of the windows of too wide widths for dataset 27\n",
      "Intersection of the windows of too wide widths for dataset 27\n",
      "Intersection of the windows of too wide widths for dataset 27\n",
      "Intersection of the windows of too wide widths for dataset 32\n",
      "Intersection of the windows of too wide widths for dataset 32\n",
      "Intersection of the windows of too wide widths for dataset 32\n",
      "Standart  -  17.87\n",
      "LowFP  -  3.44\n",
      "LowFN  -  23.2\n"
     ]
    }
   ],
   "source": [
    "# nab metric calculation\n",
    "nab = evaluating_change_point(true_cp, predicted_cp, metric='nab', numenta_time='30 sec')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Additional] localization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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